The specific aims of this application are (1) to develop a multiple logistic regression model for predicting hospital mortality of intensive care unit (ICU) patients based on information collected during the first 24 hours in the ICU; (2) to compare prospectively the accuracy and utility of three methods for predicting ICU mortality - the multiple logistic regression model, the Acute Physiology Score (APS), and the Therapeutic Intervention Scoring System (TISS) - using a cohort of approximately 2,000 ICU patients; (3) to examine daily costs incurred by ICU patients and ICU length of stay with respect to probability of survival; (4) to conduct a 6-month follow-up on all cohort members alive at hospital discharge to ascertain vital status, return-to-work status, and level of self-care. This study will be conducted prospectively on patients admitted to the ICU units at Baystate Medical Center, Springfield, MA, over a 12-month period. All study participants will be evaluated with respect to their probability of survival using the three methods noted above. The three methods will then be statistically compared on the basis of actual patient outcome. Hospital bills will be monitored for each patient and daily charges will be recorded. These will be studied with respect to their relationship to probability of survival as well as to trends observed during the course of stay in the ICU. Additionally, a 6 month post-hospital discharge follow-up will be conducted on each patient to ascertain vital status, return-to-work status, and level of self-care. An accurate, easily applied model for predicting mortality of ICU patients would have important applications to ICU management, physicians' decisions on aggressiveness of care, alternatives to intensive care, regional planning, and future research on the effectiveness of intensive care.